docs: def gather_sorting_comparison(working_folder, ground_truths, use_multi_index=True):
      """
      Loop over output folder in a tree to collect sorting from
      several sorter on several dataset and returns sythetic DataFrame with 
      several metrics (performance, run_time, ...)
      
      Use SortingComparison internally.
      
      
      Parameters
      ----------
      working_folder: str
          The folrder where sorter.run_sorters have done the job.
      ground_truths: dict
          A dict where each key is the recording label and each value 
          the SortingExtractor containing the ground truth.
      use_multi_index: bool (True by default)
          Use (or not) multi index for output dataframe.
          Multiindex is composed from (rec_name, sorter_name).
  
      Returns
      ----------
      comparisons: a dict of SortingComparison
      
      out_dataframes: a dict of DataFrame
          Return several usefull DataFrame to compare all results:
            * run_times
            * performances
      """
      
      working_folder = Path(working_folder)
      
      comparisons = {}
      out_dataframes = {}
      
      
      # get run times:
      run_times = pd.read_csv(working_folder /  'run_time.csv', sep='\t', header=None)
      run_times.columns = ['rec_name', 'sorter_name', 'run_time']
      run_times = run_times.set_index(['rec_name', 'sorter_name',])
      out_dataframes['run_times'] = run_times
      
      
      #~ columns =  ['tp_rate', 'fn_rate']
      performances = pd.DataFrame(index=run_times.index, columns=_perf_keys)
      out_dataframes['performances'] = performances
      
      results = collect_results(working_folder)
      for rec_name, result_one_dataset in results.items():
          #~ print()
          #~ print(rec_name)
          for sorter_name, sorting in result_one_dataset.items():
              #~ print(sorter_name)
              #~ print(sorting)
              
              gt_sorting = ground_truths[rec_name]
  
              comp = SortingComparison(gt_sorting, sorting, count=True)
              
              comparisons[(rec_name, sorter_name)] = comp
              
              perf = compute_performance(comp, verbose=False, output='pandas')
              
              performances.loc[(rec_name, sorter_name), :] = perf
  
      
      if not use_multi_index:
          for k, df in out_dataframes.items():
              out_dataframes[k] = df.reset_index()
      
      return comparisons, out_dataframes